OP0199 RADIOMIC SIGNATURES REFLECT TREATMENT RESPONSE TO NINTEDANIB IN PRECLINICAL LUNG FIBROSIS MODEL
Authors: D. Lauer, J. Schniering, H. Gabrys, M. Maciukiewicz, M. Brunner, O. Distler, T. Frauenfelder, S. Tanadini-Lang, B. Maurer
Journal: Annals of the Rheumatic Diseases (2022)

Background Responses to anti-fibrotic drugs in preclinical disease models are difficult to quantify by histological analysis of single tissue sections. Quantitative in-depth analysis of imaging data, termed “radiomics”, may represent a more reliable and accurate measure of treatment response since the pathology of the whole organ is captured. Objectives To study the potential of µCT-derived radiomic features to reflect response to Nintedanib in the bleomycin (BLM)-induced murine model of fibrosing interstitial lung disease. Methods All C57BL/6J mice from both study groups were intratracheally instilled with 2 U/kg BLM on day 0 to induce lung fibrosis. Nintedanib was administered daily by gavage at 60 mg/kg for two weeks starting from day 7 (n=15). Controls received equivalent treatment with vehicle-only (n=19). Whole lung µCT scans (SkyScan 1176, Bruker) of each animal were acquired at baseline (day 0), pre-treatment (day 7), and post-treatment (day 21). The Ashcroft score was assessed on Sirius Red stained lung sections post-treatment. Lung volumes in µCTs were defined semi-automatically in MIM Software (6.9.2), followed by extraction of radiomic features with our in-house developed software Z-Rad (7.3.1). Each data set contained 1’386 features, describing image characteristics with histogram, texture, and wavelet functions. Data pre-processing involved removal of features sensitive to intra- and interobserver delineation variability (ICC&lt;0.75), highly correlated features (Pearson’s r&gt;0.95), and features not significantly changing between days 0 and 7 (p&gt;0.05). Agglomerative clustering of radiomic temporal trajectories was performed on the Nintedanib group to identify distinct feature clusters. The identified feature sets were then used to plot average feature value trajectories for both study groups in each cluster. To identify features significantly different between a) Nintedanib vs. control, and b) pre- vs. post-treatment, Mann-Whitney U and Wilcoxon signed-rank tests were used, respectively. Samples were pooled from two independent experiments. Results Evaluation of tissue sections did not show a significant treatment-induced reduction of fibrosis with average Ashcroft scores of 3.7 (±1.2 s.d.) and 3.4 (±1.6 s.d.) in Nintedanib and control samples, respectively (p&gt;0.05). Radiomics data analysis revealed two feature clusters in Nintedanib samples, composed of 52 features (cluster 1) and 96 features (cluster 2), the trajectories of which were then plotted for both study groups. In cluster 1, feature value trajectories significantly decreased in both Nintedanib and control group between pre-and post-treatment (p&lt;0.001), whereas feature values in cluster 2 remained flat (p&gt;0.05). Importantly, Nintedanib-treated mice displayed a much more pronounced feature value decrease post-treatment in cluster 1 compared to the control group (p&lt;0.05). Here, feature values post-treatment resembled pre-disease baseline conditions in the Nintedanib group (p&gt;0.05), whereas the control group remained significantly different from baseline (p&lt;0.01). Cluster 1 was composed of 6 histogram, 11 texture, and 35 wavelet features, emphasizing the role of high-dimensional metrics for the detection of differences. Conclusion Histological quantification of lung fibrosis accounts only for a small fraction of the whole pathology in a spatially heterogeneous disease. We demonstrated that µCT-derived radiomic features identified significant differences on imaging level following Nintedanib treatment, which we could not reliably detect on tissue level using Ashcroft scoring. These findings hold great potential for the development of novel readouts for improved stratification of anti-fibrotic treatment effects in preclinical models. Acknowledgements This study received funding support from the Swiss Lung Association. Disclosure of Interests David Lauer Shareholder of: Roche (no relation to project), Employee of: Former employee of Roche (no relation to project), Janine Schniering: None declared, Hubert Gabrys: None declared, Malgorzata Maciukiewicz: None declared, Matthias Brunner: None declared, Oliver Distler Speakers bureau: Speaker fees in the area of systemic sclerosis and related complications from Bayer, Boehringer Ingelheim, Janssen, Medscape, Consultant of: Consultancies in the area of systemic sclerosis and its complications with Abbvie, Acceleron, Alcimed, Amgen, AnaMar, Arxx, AstraZeneca, Baecon, Blade, Bayer, Boehringer Ingelheim, Corbus, CSL Behring, 4P Science, Galapagos, Glenmark, Horizon, Inventiva, Kymera, Lupin, Miltenyi Biotec, Mitsubishi Tanabe, MSD, Novartis, Prometheus, Roivant, Sanofi and Topadur, Grant/research support from: Grant/research support from Kymera, Mitsubishi Tanabe, Boehringer Ingelheim, Thomas Frauenfelder: None declared, Stephanie Tanadini-Lang: None declared, Britta Maurer Speakers bureau: Received speaker fees from Boehringer-Ingelheim as well as congress support from Medtalk, Pfizer, Roche, Actelion, Mepha, and MSD, Consultant of: Consultancies with Novartis, Boehringer Ingelheim, Janssen-Cilag. Has a patent mir-29 for the treatment of systemic sclerosis issued (US8247389, EP2331143), Grant/research support from: Had grant/research support from AbbVie, Protagen, Novartis Biomedical Research.